“Bridges are really hard,… and there are like 500 bridges in Pittsburgh.”

Of course, it is the infinite (or near infinite) context that we, humans can process and machines aren’t even close … But, one would think bridges would be easier – no distractions, well designed straight roads; of course with the current GPS accuracy, the car might think that it is in the water and start rolling out it’s fins !!

“You have a lot of infrastructure on the bridge above the level of the car that we as humans take into account, … But when you sense those things with a sensor that doesn’t have the domain knowledge that we do … you could imagine that the girders coming up from the side of the bridge and that kind of thing would be disturbing or possibly confusing.”

In fact Pittsburgh is called “The City of Bridges”, even though some have different interpretations (we will come to that discussion in a minute)

While we are on the subject, I do have a couple of books for the Uber Car to read ! It can even order them through it’s robotic friend Alexa ! or drive to wherever fine books are sold, on it’s own time – Uber might not pay for the impromptu solo drive.

In short, a SLAM system needs known points in addition to unknown points, to reason about & figure out it’s trajectory – bridges have less of known points it can rely on …

We can definitely employ Deep Learning ConvNets as well as traditional computer vision with a dash of contextualization is a good start … that is a topic for another time (sooner than later…). Probably an interesting opportunity for bridges.ai or openbridges.org

For those snappy Machine Learning experts, there is even a Pittsburgh Bridges Data Set at UCI, to start with ! Probably nowhere near the data needed to train modern Convolutional Nets, but one can augment the images with algorithms like Flip, Jitter, Random Crop and Scale et al.

If we think Pittsburgh is difficult, wait until Uber starts autonomous driving in Amsterdam ! While Pittsburgh has 446 bridges, many sources put Amsterdam with over 1000 bridges that cars can travel. There are many bicycle and pedestrian bridges in Amsterdam that an Uber car wouldn’t be interested in – except, of course, to pick up the tired pedestrians ;o). The which-city-has-max-number-of-bridges discussions can be followed here:

Like this:

I am spending this weekend with Yann LeCun (virtually, of course) studying the excellent video Lectures and slides at the College de France. A set of 8 lectures by Yann LeCun (BTW pronounced as LuCaan) and 6 guest lectures. The translator does an excellent job – especially as it involves technical terms and concepts !

Exponential Advances:

An interesting article in Nature points out that exponential advanced in technological growth can result is a very alternate world very soon.

IBM X Prize:

And the IBM AI X Prize is offering a chance to showcase powerful ideas that tackle challenges.

Got me thinking … What do would we want our machines/AI to do ?

I am interested in your thoughts. Pl comment on what you would like AI to do.

Earlier I had written about us not wanting our machines to be like us; understand us – may be, help us – definitely, but imitate us – absolutely not …

So what does that mean ?

Driving cars ? – Definitely

Image recognition, translation and similar tasks ? – Absolutely

Write like Shakespeare just by feeding all the plays to a neural network like the LSTM ? – Definitely not !

I see folks training deep Learning systems by feeding them Shakespeare plays and see what the AI can write. Good exercise, but is that something we would get an X Prize for ? Of course, that is putting the cart before the horse !

We don’t write just by memorizing the dictionary and Elements of Style !!

We write because we have a story to tell.

The story comes before writing;

Experience & imagination comes before a story …

A good story requires both the narrative power as well as a powerful content with it’s own anti-climax, and of course the hanging chads ;o)

Which the current AI systems do not possess …

Already we have robots (Google Atlas) that can walk like a human – leaving aside the the goofy gait – which, of course, is mainly a mechanical/balance problem than an AI challenge

Robots can drive way better than a human

They translate a lot better than humans can (Of course language semantics is a lot more mechanical than storytelling)

Or is AI just a mechanical fallacy as Kasparov points out “… only intelligent the way your programmable alarm clock is intelligent“

In many ways, by helping AI to understand us, the ultimate utility might not be whether AI really comprehends us or not, but whether we get to understand us better, in the process !! And that might be the best outcome out of all of these innovations.

Over the past 100 years, we’ve been training humans to be as punctual and predictable as machines; … we’re so used to being machines at work—AI frees us up to be humans again ! – Well said SriSatish

With these points in mind, it is interesting to speculate what the AI X-Prize TED talks would look like in 2017; in 2018. And what better way to predict the future than to invent it ? I am planning on working on one or two submissions …

Good insights into what Cognitive Computing is, as a combination of Intelligence(Algorithms), Inference(Knowledge) and Interface (Visualization, Recommendation, Prediction,…)

IMHO, Cognitive Computing is more than Analytics over unstructured data, it also has touches of AI in there.

Reason being, Cognitive Computing understands humans – whether it is about buying patterns or the way different bodies reacts to drugs or the various forms of diseases or even the way humans work and interact

And that knowledge is the difference between Analytics and Cognitive Computing !

I like Cognitive Computing as an important part of AI, probably that is where most of the applications are … again understanding humans rather than being humans !

Prof.Pedro Domingos has done a masterful job of unboxing Machine Learning – and unboxing is the right word!

A very insightful book that would bring tears (of joy, not misery) to the eyes of Data Scientists and Data Engineers; not to mention the C-Suite execs who would acquire deep wisdom of the data kind (am not sure if they would shed tears, they would if they could….)

And for those who haven’t read the book yet you should run – not walk, to the nearest store (or to the nearest Amazon web site with a speedy DNS) and buy one (or more!)

While you are waiting for the book to arrive (by second day shipping – you’all have prime shipping don’t you ?) you could prime yourself for the intellectual feast by reading the two resources :

Prologue:

The book can be consumed at least at two levels – first an insight into the domain of algorithms, data and machine learning; but a more exciting level is as an inspiration and a guide post into techniques and mechanisms that augment current models one is working on – a natural extension to Prof.Domingos’ call for action …

I’d like to give you a parting gift … the great undiscovered ocean stretches into the distance, the gift is a boat-Machine Learning- and it’s time to set sail

My trek through the book – the latter, and what an incredible journey it was ! As Prof.Domingos says

Before we can learn deep truths withmachine learning, we have to discover deep truths about machine learning …

and the book does the latter – in spades!

“The society is changing, one learning algorithm at a time” – The prologue runs like a Bond movie (A Tron-esq Master Algorithm/MCP as the next head of Spectre, anyone ?) expanding this idea into various modern day successes, for example “The candidate with the best voter model wins” (Ref my blog All The President’s Data Scientists)

Main Ideas:

The main thesis of the book is around the Five Tribes of Machine learning and the Master Algorithm that unifies all (& more..) The central hypothesis of the book is like so :

All knowledge – present, past & future – can be derived from data, by a single, universal learning algorithm – the Master Algorithm

The language is poetic and picturesque, weaving through a lot of deep concepts, conveying the art of possible and the probable, tickling the imagination of the uninitiated as well as the practitioner.

The analogies are very real and reflect the fundamental principles of Machine Learning and Big data viz

“ramblings of a drunkard, locally coherent even if globally meaningless”

“MCMC as drown our sorrows in alcohol, get punch drunk & stumble around all night”

SVM as a fat snake slithering thru mine field or comparing dimensionality reduction and arranging books on a shelf !

The book is full of nuggets of wisdom and insights, let me iterate a couple:

S-curve as the basis of evolving systems “the most important curve in the world”, quoting Hemingway’s The Sun Also Rises about how he went bankrupt “Two ways – Gradually & then Suddenly!” the S curve of course. Also the S-curve, not Singularity that will explain the evolution of AI

The progression from Hopfield’s deterministic spin glass, to work on probabilistic neurons by Hinton, et al.

Nature (the program) evolves for the nurture (the data) it gets, and the Baldwin evolution ie “behaviors that are first learned become genetically hardwired” – a strong case for the important step of model evolution after deployment (I had talked about it at The Best of the Worst in Big Data – see slide #7, video of pyata talk)

Power laws, where things get better with time, “except, of course, Windows, that gets slower with every version !“

The jobs machines are good at “Credit applications and car assembly rather than stumbling around a construction site”. The key is, machines can’t be like us and vice versa; humans are good at tasks that require complex context & common sense and we don’t compete with the machines viz. “you don’t outrun a horse, you ride it!” – well said, Prof.Domingos. I also have similar thoughts about AI.

Epilogue:

Absolutely worth reading, in the genre of Stephen Bakers “Final Jeopardy” (my book review) & Stephen Levy’s “In The Plex” (my book review) to name a few. It is instructive to see how much the domain of Machine Learning has evolved in the span of ~4 years !

Trivia:

Works that blend multiple genres are hard to create but provide endless enjoyment. I enjoyed 3 in the last couple of weeks – Prof. Domingos’ The Master Algorithm, the movie Bahubali and the songs (a juxtaposition of Sanskrit/ vernacular) and of course, Spectre (the movie & the motion picture soundtrack)

And am planning on next set of book reviews – a somewhat orthogonal domain- FinTech – Actually am pursuing the MS-CFRM at UWA !

Illuminae (and S – I have both !) belong to a new meta genre – books that give you a multi-dimensional on-line experience; the inverse (or transpose – am watching MIT 18.06) of e-books, that is, you read them like an e-book, but in the physical form !

Exec Summary:

One possible trajectory and locus (“product cadence”) for Twitter 2.0 is to be a platform – to tell stories with different levels of abstraction – from basic curated signals to aggregated intelligence (ie trends, positions, sentiments and issues) & finally the higher order of exposing stratified inference built on the signals and intelligence.

For example CPC advertisers might want to know “Who is an NBA Fan” for personalized ad campaigns based on the interest graph (We did a similar project few years ago, based on Twitter data)

All without sacrificing the core Twitter consumption experience, but adding different dimensions to Twitter consumption …

What do campaigns want ?

They want curated inference (which they can directly consume for actionable outcomes) and curated intelligence (for overlaying specialized models over the exposed signals at different orders). All the President’s Data Scientists would have interesting data science models over the Twitter signals. A general model is like so:

Twitter 2.0 – Trajectory & Locus

Now Twitter is an agora for pure message-based interactions; but it has lot more potential – to be a platform (of course,without sacrificing the essential nature of the medium) ! To get there, it needs to be proactive, providing different levels of abstraction – from the basic curated signals to aggregated intelligence (ie trends, positions, sentiments and issues) and the higher level of stratified inference. It also should provide congruences on Twitter to Rest-Of-The-World ie how indicative are the twitter-verse of the general population.

Topic Streams a.k.a TweeTopics

I use Twitter for 3 things – to keep current with topics that interest me, keep in touch with friends & acquaintances and finally publish things that I am interested in – many times as a bookmark !

It is almost impossible to follow topics. The List functionality never worked for me. It should be as easy to follow & unfollow at the level of topics. In the day and age, it is not that hard to run the tweets through a set of analytics engines, cluster them by subjects and offer the topics, with the same semantics as people ! The current interaction semantics are very relevant – that is what makes Twitter Twitter.

There was some thoughts about tweet threading – I think that defeats the purpose; tweets are stateless and that attribute is very important

Twitter is different from facebook and linkedin, it is not a social graph but an interest graph. Many of the traditional network mechanisms & mechanics, like network diameter & degrees of separation, might not make sense. But, others like Cliques and Bipartite Graphs do

Topic Spaces a.k.a. TweetSpaces a.k.a TweetRooms

Twitter is the right platform for ad-hoc,ephemeral spaces to exchange quick notes.

This was my observation in 2011, and still it is true.

IM is too heavy weight and not that easy for quick things like “Where is that meeting room” or “Which seat are you in” or “What should we discuss next” et al. A one-to-many exchange, between people who are spatially (and even temporally) in separate spaces. They might in a plane, on a call or even in a hallway! Should be easy to add a “!” tag, and shout the info. Yep, folks need to know what the ! tag is. Actually come to think of it, we could have many types of tags using a lot of the ‘$’,’@’,’%’,’^’,’&’ and ‘*’ characters with different semantics!

Interface can be recommendations, financial predictions, ad forecasts or even actual devices that interface to predictive models

And business needs knobs & dials at the Inference & Interface layers

The Infrastructure then appropriately fires frameworks Hadoop or Spark or Java or iPython …

Digging deeper, Hadoop itself has three layers – none of them operable by a business user, but real work horses

HDFS – the distributed File System

MapReduce – the distributed data parallel computation engine

HBase – the NOSQL data store

Back to Andrew’s points, Hadoop (and it’s cousins) should remain as a tool for the Chefs; but diners do need to express their choices and have the ability to “tweak” the seasonings, portions or even the amount of cooking; a declarative interface (which tells what but not how) comes from the domain specific menus catered by the restaurants which focus on respective culinary styles or even a fusion !

Now I am getting Hungry ! On my way to downstairs (am at the Hilton – NY Fashion District) to my favorite Chipotle – who in fact gives me the declarative freedom, without getting into their kitchen and the need to handle the saucepans ;o) It is better that way because I am terrible with cooking and spice measures – I can tell less salt but not the amount !